Methods and apparatus to determine media exposure of a panelist

ABSTRACT

Methods, apparatus, systems and articles of manufacture to determine media exposure of a panelist are disclosed. An example apparatus include memory; computer readable instructions; and processor circuitry to execute the computer readable instructions to: determine an anonymized identifier from media monitoring data corresponding to a personal people meter of a panelist; filter anonymized census data from a plurality of media devices based on the anonymized identifier; when second media data different than first media data is included in the media monitoring data during a same time duration, tag the time duration as corresponding to multiple media exposure; and credit exposure to media for the panelist based on the tag.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 17/328,987, filed May 24, 2021, which is a continuation of U.S.patent application Ser. No. 16/230,605, filed on Dec. 21, 2018 (now U.S.Pat. No. 11,019,380, which issued May 25, 2021). Priority to U.S. patentapplication Ser. Nos. 16/230,605 and 17/328,987 is hereby claimed. U.S.patent application Ser. Nos. 16/230,605 and 17/328,987 are incorporatedherein by reference in their entireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to media monitoring and, moreparticularly, to methods and apparatus to determine media exposure of apanelist.

BACKGROUND

Media players on electronic devices (e.g., smartphones, tabletcomputers, computers, etc.) enable access to a wide range of media. Themedia can be streamed from the Internet via a browser or an applicationdedicated for streaming media or playing media. Many media streamingwebsites or applications stream advertisements along with contentselected for presentation by a viewer or machine (e.g., web crawler) oroutput of audio by a speaker or headphone. For example, if a viewerchooses to listen to a song on Spotify™, an advertisement may bestreamed in an audio player application of Spotify™ before the chosenaudio is output.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates and example environment for measuring panelistexposure to media in conjunction with teachings of this disclosure.

FIG. 2 is a block diagram of an example implementation of the panelistactivity determiner of FIG. 1 .

FIGS. 3 and 4 are flowcharts representative of example machine readableinstructions which may be executed to implement the example panelistactivity determiner of FIGS. 1 and/or 2 .

FIG. 5 is a block diagram of an example processor platform structured toexecute the instructions of FIGS. 3 and/or 4 to implement the examplepanelist activity determiner of FIGS. 1 and 2 .

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Consuming media presentations generally involves listening to audioinformation and/or viewing video information such as, for example, radioprograms, music, television programs, movies, still images, etc.Media-centric companies such as, for example, advertising companies,broadcast networks, etc. are often interested in the viewing andlistening interests of their audience to better market their productsand/or to improve their programming. A well-known technique often usedto measure the exposure and/or number of audience members exposed tomedia involves awarding media exposure credit to a media presentationfor each audience member that is exposed to the media presentation.

Media exposure credit is often measured by monitoring the mediaconsumption of audience members using, for example, portable peoplemeter (PPMs), also known as portable metering devices and portablepersonal meters. A PPM is an electronic device that is typically worn(e.g., clipped to a belt or other apparel) or carried by a panelist(e.g., an audience member) and configured to monitor media consumption(e.g., viewing and/or listening activities) using any of a variety ofmedia monitoring techniques. For example, one technique for monitoringmedia consumption involves detecting or collecting information (e.g.,ancillary codes, signatures, etc.) from audio and/or video signals thatare emitted or presented by media presentation devices (e.g.,televisions, stereos, speakers, computers, video display devices, videogames, mobile telephones, etc.) and comparing the collected informationto reference information to identify the media. Panelists are personsthat have agreed to be monitored by, for example, an audiencemeasurement entity (AME) such as The Nielsen Company (U.S.), LLC.Typically, such panelists provide detailed demographic information(e.g., race, age, income, home location, education level, gender, etc.)when they register to participate in the panel.

While wearing a PPM, an audience member or monitored individual performstheir usual daily routine, which may include listening to the radioand/or other sources of audio media and/or watching television programsand/or other sources of visual media. As the audience member is exposedto (e.g., views, listens to, etc.) media, a PPM associated with (e.g.,assigned to and carried by) that audience member detects audio and/orvideo information associated with the media and generates monitoringdata. In general, monitoring data may include any information that isrepresentative of (or associated with) and/or that may be used toidentify a particular media presentation (e.g., a song, a televisionprogram, a movie, a video game, etc.) and/or to identify the source ofthe media presentation (e.g., a television, a digital video disk player,a stereo system, etc.). For example, the monitoring data may includesignatures that are collected or generated by the PPM based on themedia, audio codes that are broadcast simultaneously with (e.g.,embedded in) the media, infrared (IR) or radio frequency (RF) signalsemitted by a remote control device and/or emitted by a transceiverconfigured to transmit location information, information supplied by theaudience member using any of a variety of data input devices, etc.

Although PPMs measurement can generate accurate media monitoring data byextracting codes and/or generating signatures from ambient audio togenerate media monitoring data (e.g., data corresponding to media towhich a panelist was exposed), a PPM can only generate media monitoringdata for audio that is loud enough to be detected by a sensor of the PPMand/or video that is within range of a camera or sensor of the PPM(e.g., when the PPM is implemented with a camera in a headset, forexample). Accordingly, if the panelist listens to audio (e.g., music,commercials, radio, and/or audio of a video) from a media device (e.g.,a smart phone, tablet, laptop, etc.) while audio can not be ambientlycollected (e.g., because the panelist is listening with headphones),such media exposure will be absent from the media monitoring datagenerated based on the PPM (e.g., because a PPM may not sense audio froma headphone). Additionally, if the panelist is using PPM (e.g., a smartheadset, Google Glass, etc.) that includes a camera to track mediaexposure, if the headset is off or not directed at the media, such mediaexposure will be absent from the media monitoring data generated by thePPM. Some PPMs include a headphone jack for tracking audio output by amedia device. However, panelist compliance with the headphone jack ofthe PPM is typically low. Further, to reduce the size and/or weight ofthe PPM and/or to waterproof the PPM, next generation PPMs may notinclude a headphone jack. Accordingly, examples disclosed hereinsupplement incomplete media monitoring data using software developmentkit (SDK) census data.

When an audience measurement entity (AME) partners with a media provider(e.g., Spotify®, Shoutcast®, Stitcher®, Netflix®, YouTube®, Hulu®,Pandora®, Last.fm®, etc.), the media provider may agree to let the AMEinstall an SDK on an application, device, or website of the mediaprovider or other entity. The SDK monitors user exposure to the media.For example, when a user streams a song, watches a video, views anadvertisement, etc. on the application or website, the SDK tracks themedia exposure and transmits the tracked media exposure information tothe AME. To preserve the identity/anonymity of the media provider users,the SDK generates an anonymous identifier so that the AME does not knowthe real identifier of the user. The anonymous media exposureinformation is herein referred to as SDK census data. A panelist may beexposed to media of the media provider on a media device usingheadphones. As a result, the SDK census data may include the informationthat the incomplete media monitoring data corresponding to a PPM ismissing. However, because the SDK census data is anonymized with the SDKcensus identifier, examples disclosed herein link the panelistidentifier to the SDK census identifier at the media device of apanelist using an AME application running on the media device. In thismanner, examples disclosed herein can leverage the SDK census datacorresponding to a panelist to backfill incomplete media monitoring datacorresponding to a PPM of the panelist.

FIG. 1 illustrates and example environment 100 for measuring panelistexposure to media in conjunction with teachings of this disclosure. Theexample environment 100 includes an example media provider 102, anexample panelist 104, an example non-panelist 106, example mediadevice(s) 108, 110, an example PPM 111, example wireless interfaces 112,example SDKs 114, an example Bluetooth interface 116, an example AMEapplication 118, an example network 122, an example AME 124, an examplepanelist activity determiner 126, and an example database 128.

The media provider 102 of the illustrated example of FIG. 1 correspondsto any one or more media provider(s) capable of providing media forpresentation via the media devices 108, 110. The media provided by themedia provider 102 can provide any type(s) of media, such as audio,video, multimedia, etc. Additionally, the media can correspond to livemedia, streaming media, broadcast media, stored media, on-demandcontent, etc. In some examples, the media provider 102 of theillustrated example of FIG. 1 is a server providing Internet media(e.g., web pages, audio, videos, images, etc.). The media provider 102may be implemented by a digital broadcast provider (cable televisionservice, fiber-optic television service, etc.) and/or an on-demanddigital media provider (e.g., Internet streaming video and/or audioservices such as Spotify®, Shoutcast®, Stitcher®, Netflix®, YouTube®,Hulu®, Pandora®, Last.fm®, etc.) and/or any other provider of streamingmedia services. In some other examples, the media provider 102 is a hostfor web site(s). Additionally or alternatively, the media provider(s)102 may not be on the Internet. For example, the media provider may beon a private and/or semi-private network (e.g., a LAN, a virtual privatenetwork) to which the media device(s) 108, 110 connect via the examplenetwork 122.

The panelist 104 of FIG. 1 is a person that has agreed to be monitoredby the AME 124. When the panelist 104 agrees to be part of a panel, thepanelist 104 wears or carries the example PPM 111 to monitor exposure toaudio of media, as further described below. Additionally, the panelist104 is required to install the example AME application 118 on the mediadevice 108 to be able to transmit monitored audio data from the PPM 111to the AME 124 via the media device 108, as further described below. Thenon-panelist 106 is not a member of the panel. Rather, the non-panelist106 is an audience member that may access media provided by the mediaprovider 102 on the media device 110.

The media devices 108, 110 of FIG. 1 are devices that retrieve mediafrom the media provider 102 for presentation or output. In someexamples, the media devices 108, 110 are capable of directly presentingmedia (e.g., via a display or internal speakers) while, in some otherexamples, the media devices 108, 110 present the media on separate mediapresentation equipment (e.g., external speakers, an external display,headphones/earphone, earbuds, etc.). For example, the media devices 108,110 of the illustrated example are mobile phones (e.g., smart phones),and thus, are capable of outputting media (e.g., via an integrateddisplay and speakers) and/or may output the media using an externaldevice (e.g., connected headphones, connected speakers, Bluetoothspeakers, connected display, etc.). Alternatively, either of the mediadevices 108, 110 may be a gaming console (e.g., Xbox®, Playstation® 3,etc.), a streaming media device (e.g., a Google Chromecast, an Apple TV)a smart media device (e.g., an iPad, a tablet, etc.), digital mediaplayers (e.g., a Roku® media player, a Slingbox®, etc.), a smarttelevision, a computing device, etc. The example media devices 108, 110may utilize an application to access media from the media provider 102via the network 122. For example, the media devices 108, 110 may use aSpotify® application to stream music provided by the media provider 102.As further described below, the media device 108 of the panelist 104includes the wireless interface 112, the SDK 114, the Bluetoothinterface 116, and the AME application 118. The media device 110 of thenon-panelist 106, includes the wireless interface 112 and the SDK 114.

The example PPM 111 of FIG. 1 is a device that may be carried or worn bythe example panelist 104 as shown in FIG. 1 . In particular, the examplePPM 111 may be configured to monitor media to which the panelist 104 isexposed using one or more media detection devices. For example, the PPM111 may include one or more media detection devices (e.g., sensor(s),microphone(s), camera(s), etc.) used to detect presented media andgenerate or collect media monitoring information or media-related databased on, for example, audio signals, visual signals, radio frequencysignals, etc. In some examples, the PPM 111 may collect media monitoringinformation (e.g., ancillary codes, signatures, etc.) associated withany media (e.g., video, audio, movies, music, still pictures,advertising, computer information, etc.) to which the panelist 104 isexposed. For example, the PPM 111 may be configured to obtain audiocodes (e.g., watermarks), generate or collect signatures, fingerprints,etc. that may be used to identify video programs (e.g., DVD movies,steaming video, television programming, etc.), audio programs (e.g., CDaudio, steaming audio, radio programming, etc.), advertisements, etc. bysensing ambient audio. In another example, the PPM 111 may include acamera or other sensor to obtain video codes (e.g., watermarks),generate or collect video signature, fingerprints, etc. that may be usedto identify video programs, audio programs, advertisements, etc. bysensing ambient video using the camera or sensor. In some examples, thePPM 111 and/or the media device 108 may identify the media based on thecodes embedded in the media and/or the signatures generated based on themedia. For example, the PPM 111 and/or media device 108 may compare theobtained codes and/or generated signatures to a database of referencecodes and/or reference signatures to identify a match corresponding toparticular media. In such examples, the identified media is included inmedia monitoring data that may be transmitted to the example AME 124 forfurther analysis/processing (e.g., to credit exposure to the media). Insome examples, the PPM 111 forwards the obtained codes and/or generatedsignatures to the media device 108 to transmit to the AME 124 (e.g., asunprocessed media monitoring data). In such examples, the AME 124pre-processes the unprocessed media monitoring data to identify themedia corresponding to the obtained codes and/or generated signatures atthe AME 124 prior to crediting the media.

The example wireless interfaces 112 of FIG. 1 transmit and receive datavia wireless communications (e.g., a cellular communication, a Wi-Ficommunication, etc.) using the example network 122. For example, thewireless interface 112 may receive media from the example media provider102 to be presented/output by the media device 108 and/or an externalcomponent connected (e.g., via a wired or wireless connection) to themedia device 108. Additionally, the wireless interface 112 may transmitpanelist data (e.g., media monitoring data) and/or SDK census data tothe example AME 124 via the network 122.

The example SDK 114 of FIG. 1 is a program owned by the example AME 124that runs on the example media device 108 in conjunction with the mediaprovider 102. For example, the AME 124 and the media provider 102 enterinto an agreement under which the AME 124 is allowed to utilize the SDK114 whenever media provided by the media provider 102 is output by themedia device 108. Accordingly, when a user (e.g., the panelist 104and/or the non-panelist 106) watches video and/or listens to audioprovided by the example media provider 102 using the media device 108,the SDK 114 monitors the audio and/or video being output by the mediadevice 108 and/or a device (e.g., display, speaker, headphone, etc.)connected to the media device 108. Periodically, aperiodically, or basedon a schedule, the SDK 114 transmits SDK census data corresponding tothe monitored media (e.g., identifying video and/or audio output by themedia device 108) to the AME 124 using the wireless interface 112. Topreserve the privacy of the user, the SDK 114 anonymizes the data byrunning an algorithm to generate an anonymized identifier (e.g., acensus identifier) that is included with the SDK census data. In thismanner, the AME 124 can determine to what media a user has been exposedwithout identifying the user.

The example Bluetooth interface 116 of FIG. 1 receives media monitoringdata (e.g., processed or unprocessed) from the PPM 111 via wirelesscommunications (e.g., Bluetooth, Bluetooth Low Energy, ZigBee, etc.).For example, if the PPM 111 obtains codes and/or generates signatures ofambient media and does not process the codes and/or signatures toidentify media, the PPM 111 transmits unprocessed media monitoring datato the media device 108 via the Bluetooth interface 116. In anotherexample, if the PPM 111 obtains codes and/or generates signatures ofambient media and processes the codes and/or signatures to identify themedia, the PPM 111 transmits processed media monitoring data to themedia device 108 via the Bluetooth interface 116. In some examples, themedia monitoring data includes a timestamp and/or location datacorresponding to when and/or where the code was extracted or thesignature was generated.

The AME application 118 of FIG. 1 obtains the media monitoring data fromthe example PPM 111 via the example Bluetooth interface 116. In someexamples, when the media monitoring data is unprocessed, the AMEapplication 118 may process the unprocessed media monitoring data todetermine the media corresponding to the obtained code and/or generatedsignature to generate processed media monitoring data. The AMEapplication 118 adds a panelist identifier to the media monitoring data.In this manner, when the media monitoring data is transmitted to theexample AME 124, the AME 124 can credit the panelist 104 based on themedia monitoring data. Additionally, the AME application 118 may performthe same algorithm as the example SDK 114 to generate the censusidentifier generated by the SDK 114. The AME application 118 adds thecensus identifier to the media monitoring data. In this manner, the AME124 can identify which data from the SDK census data received by the AME124 corresponds to the panelist 104, as further described below. Theexample AME application 118 transmits the media monitoring dataincluding the panelist identifier and the census identifier to theexample AME 124 via the example network 122 using the example wirelessinterface 112.

The example network 122 of the illustrated example of FIG. 1 is network,such as the Internet, a wireless mobile telecommunications network(e.g., 2G, 3G, LTE, etc.), and/or a cellular network. However, theexample network 122 may be implemented using any suitable wired and/orwireless network(s) including, for example, one or more data buses, oneor more Local Area Networks (LANs), one or more wireless LANs, one ormore cellular networks, one or more private networks, one or more publicnetworks, etc. The example network 122 enables the media provider 102,the media devices 108, 110, and the AME 124 to communicate data (e.g.,media, media monitoring data, SDK census data, etc.) between each other.

The example AME 124 of FIG. 1 is a central facility that may beimplemented by a server that collects and processes media monitoringdata and/or SDK census data from the media devices 108, 110 to generateexposure metrics related to media presented to the panelist 104. The AME124 analyzes the media monitoring information to identify, for example,which media presentation devices are the most owned, the most-frequentlyused, the least-frequently owned, the least-frequently used, themost/least-frequently used for particular type(s) and/or genre(s) ofmedia, and/or any other media statistics or aggregate information thatmay be determined from the data. The media monitoring information mayalso be correlated or processed with factors such as geodemographic data(e.g., a geographic location of the media exposure measurement location,age(s) of the panelist(s) associated with the media exposure measurementlocation, an income level of a panelist, etc.) Media presentation deviceinformation may be useful to manufacturers and/or advertisers todetermine which features should be improved, determine which featuresare popular among users, identify geodemographic trends with respect tomedia presentation devices, identify market opportunities, and/orotherwise evaluate their own and/or their competitors' products. Theexample AME 124 includes the example panelist activity determiner 126and the example database 128. As further described above, the AME 124may process unprocessed media monitoring data from the media device 108of the panelist 104 in order to identify media to which the panelist 104was exposed based on codes extracted by and/or signatures generated bythe PPM 111 in conjunction with ambient media.

The example panelist activity determiner 126 of FIG. 1 determines towhat media the panelist 104 was exposed by backfilling incomplete mediamonitoring data generated by the PPM 111 with SDK census datacorresponding to the panelist 104. As described above, media monitoringdata generated by the PPM 111 is incomplete when the panelist 104listens to audio output by the media device 108 using wired or wirelessheadphones, earphones, ear buds, etc. of when the panelist 104 watches avideo output by the media device 108 while the PPM is not facing thevideo, because the audio output by such devices is not loud to bedetected by a sensor of the PPM 111 or the video output by such devicesis not detected by a camera or sensor of the PPM 111. Accordingly, mediamonitoring information is absent for any audio to which the panelist 104was exposed via the media device 108 when the panelist 104 utilizesheadphones. To increase the accuracy of media crediting, the examplepanelist activity determiner 104 backfills the media monitoring data byidentifying SDK census data that corresponds to the panelist 104 andadding media exposure information based on a comparison of the SDKcensus data and the media monitoring data. Because the SDK census datais anonymous and transmitted from media devices of panelists andnon-panelists (e.g., a universe of users), the example panelist activitydeterminer 126 uses the SDK identifier of the media monitoring data toobtain SDK information corresponding to the media device 108 of thepanelist 104. Once the panelist activity determiner 126 backfills theincomplete media monitoring data of the panelist 104 with thecorresponding SDK census information, the panelist activity determiner126 credits the panelist 104 for exposure to media based on thebackfilled media monitoring data. In some examples, the example panelistactivity determiner 126 may credit the media exposure based on aduration of time (e.g., an hour, a day, a week, a month, a year, etc.).The example panelist activity determiner 126 stored the backfilled mediamonitoring data corresponding to the panelist 104 and/or creditingresults in the example database 128. An example implementation of theexample panelist activity determiner 126 is further described below inconjunction with FIG. 2 .

In the illustrated example of FIG. 1 , the AME 124 includes the exampledatabase 128 to record backfilled media monitoring data and/or creditingresults corresponding to panelists (e.g., including the example panelist104). The database 128 may be implemented by a volatile memory (e.g., aSynchronous Dynamic Random Access Memory (SDRAM), Dynamic Random AccessMemory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM), etc.) and/ora non-volatile memory (e.g., flash memory). The database 128 mayadditionally or alternatively be implemented by one or more double datarate (DDR) memories, such as DDR, DDR2, DDR3, DDR4, mobile DDR (mDDR),etc. The database 128 may additionally or alternatively be implementedby one or more mass storage devices such as hard disk drive(s), compactdisk drive(s), digital versatile disk drive(s), solid-state diskdrive(s), etc. While in the illustrated the example database 128 isillustrated as a single database, the database 128 may be implemented byany number and/or type(s) of databases. Furthermore, the data stored inthe database 128 may be in any data format such as, for example, binarydata, comma delimited data, tab delimited data, structured querylanguage (SQL) structures, etc.

FIG. 2 is a block diagram of an example implementation of the panelistactivity determiner 126 of FIG. 1 . The example panelist activitydeterminer 126 of FIG. 2 includes an example interface 200, an exampledata extractor 202, an example filter 204, an example panelist datasupplementer 206, and an example media creditor 208.

The example interface 200 of FIG. 2 receives data (e.g., mediamonitoring data and/or SDK census data) from the example media devices108, 110 via the network 122 of FIG. 1 . The interface 200 may receivethe data directly or indirectly (e.g., via radio architecture and/or aprocessor) from the devices 108, 110. In some examples, the device 108transmits unprocessed media monitoring data (e.g., codes and/orsignatures that have not been processed to identify correspondingmedia). In such examples, the AME 124 may include software, firmware,and/or hardware to process the unprocessed media monitoring data toidentify the corresponding media prior to transmitting the processedmedia monitoring (e.g., the identified media with timestamps andidentifiers) to the example interface 200. Additionally, the interface200 may transmit supplemented panelist data (e.g., panelist data backfilled with corresponding SDK census data) and/or media crediting datato the example database 128 to be stored.

The example data extractor 202 of FIG. 2 processes the media monitoringdata and the SDK data to extract identifiers (e.g., a panelistidentifier and census identifiers). For example, as described above, themedia monitoring data includes a panelist identifier (e.g., a number ofcode corresponding to the identification of the panelist 104) and an SDKcensus identifier (e.g., generated by the AME application 118 in thesame manner as the SDK 114). Accordingly, the data extractor 202extracts the panelist identifier to identify the panelist 104corresponding to the media monitoring data and extracts the censusidentifier so that the filter 204 can filter through the SDK data toobtain SDK data corresponding to the panelist 104 (e.g., media that wasoutput by the example media device 108 provided by the media provider102).

The example filter 204 of FIG. 2 utilizes the census identifierextracted from the media monitoring data by the data extractor 202 tofilter through the SDK census data to identify SDK census datacorresponding to the panelist 104. For example, the filter 204 mayfilter out SDK census data that does not correspond to the extractedcensus identifier. In this manner, the filtered SDK census datacorresponds to media that was output by the media device 108 of thepanelist 104.

The SDK data corresponding to the panelist 104 may or may not includemedia identified in the media monitoring data. For example, if the mediadevice 108 is playing audio provided by the media provider 102 (e.g.,which is included in the SDK census data) using speakers included inand/or attached to media device 108, the PPM 11 may sense the outputteddata and extract a code corresponding to the media. Accordingly, themedia monitoring data identifies the media based on the code and the SDKcensus data will include the media. However, if the audio/video isoutput by the media device 108 with headphones or while the volume ofthe phone is low or off, the PPM 111 cannot sense the audio.Accordingly, the media monitoring data does not include the media butthe SDK information does include the media.

The example panelist data supplementer 206 of FIG. 2 back fills missingmedia monitoring data for the panelist 104 using the filtered SDK data(e.g., the SDK census data corresponding to the panelist 104). Becausemedia identified in the filtered SDK data may or may not be included inthe media monitoring data, the panelist data supplementer 206 mayprocess both the media monitoring data and the filtered SDK data todetermine what data should or should not be included in the mediamonitoring data. For example, if the panelist data supplementer 206determines that both the media monitoring data and the filtered SDKcensus data identifies a song at a particular duration of time (e.g.,the media device 108 is outputting the song via a speaker that is sensedby the PPM 111), the panelist data supplementer 206 only identifies thesong once to not double count the song in the media monitoring data. Ifthe panelist data supplementer 206 determines that the SDK census dataidentifies a song at a particular duration of time that is not includedin the media monitoring data at the particular duration of time (e.g.,the song played on the media device 108 and output via headphones, sothat the PPM 111 could not sense the song), the panelist datasupplementer 206 may add the media information from the SDK census datato the media monitoring data. In such examples, the panelist datasupplementer 206 may discard the SDK census data in certain pre-definedsituations. For example, if the panelist 104 accidently allowed music toplay on the media device 108 for many hours while not actually listeningto the headphones, the SDK census data may not be representative ofactual media exposure. Accordingly, the example panelist datasupplementer 206 may process the SDK census data and discard all or partof SDK data that corresponds to media that have been played for over athreshold amount of time. If the panelist data supplementer 206determines that the media monitoring data and the filtered SDK censusdata identify different media at a particular duration of time (e.g.,the panelist 104 is listening to a song output by the media device 108is outputting the song via a speaker while watching an advertisement ona television that was sensed by the PPM 111), the panelist datasupplementer 206 may add the song identified by the SDK data to themedia monitoring device but tag the duration of time as a period of timecorresponding to multiple media exposure. The backfilled mediamonitoring data may be stored in the example database 128 of FIG. 1 .

Once the media monitoring data has been backfilled using the SDK censusdata, the example media creditor 208 of FIG. 2 credits the mediaexposure to the panelist 104 based on the panelist identification of themedia monitoring data. The media creditor 208 may generate a reportcorresponding to media exposure of the panelist 104 broken up by anyduration of time (e.g., a day, a week, a month, etc.). The mediacreditor 208 credits the media exposure to provide information toadvertisers, performance rights organizations, etc. and/or may be usedto determine/estimate statistics related to the universe of users (e.g.,unique audiences). Crediting data may be stored in the example database128 of FIG. 1 .

While an example manner of implementing the example panelist activitydeterminer 126 of FIG. 1 is illustrated in FIG. 2 , one or more of theelements, processes and/or devices illustrated in FIG. 2 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example interface 200, the example dataextractor 202, the example filter 204, the example panelist datasupplementer 206, the example media creditor 208, and/or, more generallythe panelist activity determiner 126 of FIG. 2 may be implemented byhardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the exampleinterface 200, the example data extractor 202, the example filter 204,the example panelist data supplementer 206, the example media creditor208, and/or, more generally the panelist activity determiner 126 of FIG.2 could be implemented by one or more analog or digital circuit(s),logic circuits, programmable processor(s), programmable controller(s),graphics processing unit(s) (GPU(s)), digital signal processor(s)(DSP(s)), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example interface 200, the exampledata extractor 202, the example filter 204, the example panelist datasupplementer 206, the example media creditor 208, and/or, more generallythe panelist activity determiner 126 of FIG. 2 is and/or are herebyexpressly defined to include a non-transitory computer readable storagedevice or storage disk such as a memory, a digital versatile disk (DVD),a compact disk (CD), a Blu-ray disk, etc. including the software and/orfirmware. Further still, the example panelist activity determiner 126 ofFIG. 2 may include one or more elements, processes and/or devices inaddition to, or instead of, those illustrated in FIG. 2 , and/or mayinclude more than one of any or all of the illustrated elements,processes and devices. As used herein, the phrase “in communication,”including variations thereof, encompasses direct communication and/orindirect communication through one or more intermediary components, anddoes not require direct physical (e.g., wired) communication and/orconstant communication, but rather additionally includes selectivecommunication at periodic intervals, scheduled intervals, aperiodicintervals, and/or one-time events.

Flowcharts representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the example panelist activitydeterminer 126 of FIG. 2 are shown in FIGS. 3-4 . The machine readableinstructions may be an executable program or portion of an executableprogram for execution by a computer processor such as the processor 512shown in the example processor platform 500 discussed below inconnection with FIG. 5 . The program may be embodied in software storedon a non-transitory computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associatedwith the processor 512, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor 512and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchartsillustrated in FIGS. 3-4 , many other methods of implementing theexample panelist activity determiner 126 of FIG. 2 may alternatively beused. For example, the order of execution of the blocks may be changed,and/or some of the blocks described may be changed, eliminated, orcombined. Additionally or alternatively, any or all of the blocks may beimplemented by one or more hardware circuits (e.g., discrete and/orintegrated analog and/or digital circuitry, an FPGA, an ASIC, acomparator, an operational-amplifier (op-amp), a logic circuit, etc.)structured to perform the corresponding operation without executingsoftware or firmware.

As mentioned above, the example process of FIGS. 3-4 may be implementedusing executable instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C.

FIG. 3 is an example flowchart 300 representative of example machinereadable instructions that may be executed by the example panelistactivity determiner 126 of FIGS. 1 and/or 2 to supplement mediamonitoring data of the PPM 111 with SDK census data. Although theflowchart 300 of FIG. 3 is described in conjunction with panelistactivity determiner 126 of FIGS. 1 and/or 2 , other type(s) of panelistactivity determiner(s), and/or other type(s) of processor(s) may beutilized instead.

At block 302, the example interface 200 obtains SDK census data from auniverse of media devices corresponding to a duration of time (e.g., aday, a week, a month). The duration of time may be based on user,customer, and/or manufacturer preferences. At block 304, the exampleinterface 200 obtains media monitoring data from the media device 108 ofthe example panelist 104 corresponding to the duration of time. Asdescribed above, the media monitoring data may be missing media exposureinformation when the panelist 104 listens to audio output by the mediadevice 108 using headphones and/or watches video while the PPM 111 isnot facing the media device 108, for example.

At block 306, the example data extractor 202 determines a SDK censusidentifier and a panelist identifier based on the media monitoring data.For example, the data extractor 202 may extract the panelist identifierand the panelist identifier from the media monitoring data. At block308, the example data extractor 202 identifies the panelist based on thepanelist identifier. In this manner, the media monitoring data can beused to credit the correct panelist 104.

At block 310, the example filter 204 filters the SDK census data basedon the determined census identifier to obtain SDK data corresponding tothe panelist 104. For example, the filter 204 may filter out all SDKcensus data that does not correspond to the determined censusidentifier. Because the AME application 118 generated the determinedcensus identifier in the same manner as the SDK 114, the determinedcensus identifier corresponds to SDK census data of the panelist 104.

At block 312, the example panelist data supplementer 206 backfills themedia monitoring data for the panelist 104 with the filtered SDK censusdata, as further described below in conjunction with FIG. 4 . At block314, the example media creditor 208 credits the panelist 104 forexposure to media for the duration of time based on the backfilled mediamonitoring data.

FIG. 4 is an example flowchart 312 representative of example machinereadable instructions that may be executed by the example panelistactivity determiner 126 of FIGS. 1 and/or 2 to backfill media monitoringdata for the panelist 104 with the filtered SDK data, as described abovein conjunction with block 312 of FIG. 3 . Although the flowchart 312 ofFIG. 4 is described in conjunction with panelist activity determiner 126of FIGS. 1 and/or 2 , other type(s) of panelist activity determiner(s),and/or other type(s) of processor(s) may be utilized instead.

At block 402, the example panelist data supplementer 206 selects a firstsub-duration of the duration of time (e.g., a one minute increment orany sub-duration of time) corresponding to media exposure in thefiltered SDK data. For example, if during a particular day, the mediadevice 108 only accessed media from the media provider 102 to play from1:00 PM to 1:30 PM, there will only be SDK census data from the mediadevice 108 corresponding to the duration of time between 1:00 PM and1:30 PM. In such an example, the example panelist data supplementer 206may select the SDK data from the first minute of the 1:00-1:30 durationof time.

At block 404, the example panelist data supplementer 206 determinesfirst media from (e.g., identified in) the filtered SDK data at theselected sub-duration. For example, the panelist data supplementer 206may determine that the SDK data identifies a first song during theselected sub-duration of time. At block 406, the example panelist datasupplementer 206 determines if the first media is included in the mediamonitoring data at the selected sub-duration. For example, if the SDKdata of the sub-duration correspond to a song, the panelist datasupplementer 206 may determine whether the media monitoring datacorresponds to the song at the sub-duration of time (e.g., based on amedia identifier and corresponding timestamp of the media monitoringdata). The first media being included in the media monitoring data andthe SDK data corresponds to the media output device 108 outputting themedia via speakers and being sensed by the PPM 111.

If the example panelist data supplementer 206 determines that the firstmedia is included in the media monitoring data at the selectedsub-duration (block 406: YES), the panelist data supplementer 206maintains identification of the first media as part of the mediamonitoring data at the selected sub-duration of time (block 408). Forexample, the panelist data supplementer 206 may discard the first mediadata from the SDK census data, as to not double count the first media.

If the example panelist data supplementer 206 determines that the firstmedia is not included in the media monitoring data at the selectedsub-duration (block 406: NO), the panelist data supplementer 206 addsthe first media data as part of the media monitoring data at theselected sub-duration of time (block 410) (e.g., because the media wasoutput by the example media device 108 but not sensed by the PPM 111).Alternatively, in some examples, the panelist data supplementer 206 maydiscard the first media information by determining if the applicationcorresponding to the SDK data was playing for more than a thresholdduration of time. As described above in conjunction with FIG. 2 , if themedia device 108 is unintendedly left on, the SDK may include media datacorresponding to media to which the panelist 104 was not actuallyexposed. The probability that the panelist 104 was not exposed to mediaoutput by the media device 108 increases as the amount of time that themedia device 108 outputs media increases. Thus, the threshold maycorrespond to a threshold corresponding to statistical analysis.

At block 412, the example panelist data supplementer 206 determines ifsecond media (e.g., different than the first media) is included in themedia monitoring data. Second media may be included in the mediamonitoring data when, for example, the panelist 104 is exposed to thefirst media via the media device 108 and the second media via a secondmedia device (e.g., a television, radio, etc.) and sensed by the PPM 111of FIG. 1 .

If the example panelist data supplementer 206 determines that the secondmedia is not included in the media monitoring data (block 412: NO), theprocess continues to block 416. If the example panelist datasupplementer 206 determines that the second media is included in themedia monitoring data (block 412: YES), the panelist data supplementer206 tags the selected sub-duration as corresponding to a multiple mediaexposure (block 414). In this manner, the media creditor 208 candetermine how to credit the first and/or second media (e.g., creditboth, none, or one of the first or second media) based on user,customer, and/or manufacturer preferences. At block 416, the examplepanelist data supplementer 206 determines if additional media exposuredata in the filtered SDK data at a subsequent sub-duration of time ofthe duration of time. For example, if the first iteration corresponds toa first minute of SDK census data from 1:00-1:30, the example panelistdata determiner 206 determines that there is additional media exposuredata in the filtered SDK data at a subsequent duration of time of theduration of time (e.g., the second minute of the 1:00-1:30 duration).

If the example panelist data supplementer 206 determines that there isno additional media exposure data in the filtered SDK data at asubsequent sub-duration of time of the duration of time (block 416: NO),the process returns to block 314 of FIG. 3 . If the example panelistdata supplementer 206 determines that there is additional media exposuredata in the filtered SDK data at a subsequent sub-duration of time ofthe duration of time (block 416: YES), the example panelist datasupplementer 206 selects the subsequent sub-duration of the duration ofthe duration of time corresponding to the media exposure in the filteredSDK data (block 418) and the process returns to block 404 to determinehow to continue to backfill media monitoring with the SDK data at thesubsequent sub-duration.

FIG. 5 is a block diagram of an example processor platform 500structured to execute the instructions of FIG. 3-4 to implement theexample panelist activity determiner 126 of FIG. 2 . The processorplatform 500 can be, for example, a server, a personal computer, aworkstation, a self-learning machine (e.g., a neural network), a mobiledevice (e.g., a cell phone, a smart phone, a tablet such as an iPad™),or any other type of computing device.

The processor platform 500 of the illustrated example includes aprocessor 512. The processor 512 of the illustrated example is hardware.For example, the processor 512 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor 512 implements the example interface 200,the example data extractor 202, the example filter 204, the examplepanelist data supplementer 206, and the example media creditor 208 ofFIG. 2 .

The processor 512 of the illustrated example includes a local memory 513(e.g., a cache). The processor 512 of the illustrated example is incommunication with a main memory including a volatile memory 514 and anon-volatile memory 516 via a bus 518. The volatile memory 514 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®) and/or any other type of random access memory device. Thenon-volatile memory 516 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 514, 516is controlled by a memory controller.

The processor platform 500 of the illustrated example also includes aninterface circuit 520. The interface circuit 520 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 522 are connectedto the interface circuit 520. The input device(s) 522 permit(s) a userto enter data and/or commands into the processor 512. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 524 are also connected to the interfacecircuit 520 of the illustrated example. The output devices 524 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 520 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 520 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 526. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 500 of the illustrated example also includes oneor more mass storage devices 528 for storing software and/or data.Examples of such mass storage devices 528 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 532 of FIGS. 3-4 may be stored inthe mass storage device 528, in the volatile memory 514, in thenon-volatile memory 516, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been to determine mediaexposure of a panelist by backfilling media monitoring datacorresponding to a PPM with SDK census data. The disclosed methods,apparatus and articles of manufacture provide more accurate and completemedia exposure data of panelist, thereby providing more accurate mediacrediting and/or more accurate statistical estimations of media exposurefor a universe of users. Disclosed methods, apparatus and articles ofmanufacture are accordingly directed to one or more technicalimprovement(s) to providing more accurate and complete media monitoringdata by leveraging SDK census data from panelists.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An audience measurement computing systemcomprising: at least one processor; memory having stored thereincomputer readable instructions that, when executed by the at least oneprocessor, cause the audience measurement computing system to perform aset of operations including: determining that census data correspondingto a panelist includes first media data that is not included in mediamonitoring data for the panelist during a first time period; responsiveto determining that the census data corresponding to the panelistincludes first media data that is not included in the media monitoringdata for the panelist, generating backfilled media monitoring data forthe panelist by supplementing the media monitoring data for the panelistduring the first time period with the first media data; determining,based on media data in at least one of the census data and the mediamonitoring data, that the panelist is associated with simultaneousexposure to multiple distinct media sources during a second time period;and responsive to determining that the panelist is associated withsimultaneous exposure to multiple distinct media sources during thesecond time period, augmenting the backfilled media monitoring data forthe panelist to indicate that the second time period includedsimultaneous media exposures to multiple distinct media sources.
 2. Theaudience measurement computing system of claim 1, wherein the operationsfurther include obtaining the census data corresponding to the panelistby filtering anonymized census data using an anonymized identifiercorresponding to the panelist.
 3. The audience measurement computingsystem of claim 2, wherein the operations further include determiningthe anonymized identifier corresponding to the panelist.
 4. The audiencemeasurement computing system of claim 2, wherein the anonymized censusdata corresponds to media accessed by a plurality of media devices, theanonymized census data including anonymized identifiers for theplurality of media devices.
 5. The audience measurement computing systemof claim 1, wherein the operations further include crediting mediaexposure for the panelist based on the augmented backfilled mediamonitoring data.
 6. The audience measurement computing system of claim1, wherein the operations further include obtaining the media monitoringdata for the panelist from a meter device associated with the panelist.7. The computing system of claim 6, further including the meter device,the meter device configured to generate the media monitoring data basedon ambient audio samples.
 8. A method comprising: determining thatcensus data corresponding to a panelist includes first media data thatis not included in media monitoring data for the panelist during a firsttime period; responsive to determining that the census datacorresponding to the panelist includes first media data that is notincluded in the media monitoring data for the panelist, generatingbackfilled media monitoring data for the panelist by supplementing themedia monitoring data for the panelist during the first time period withthe first media data; determining, based on media data in at least oneof the census data and the media monitoring data, that the panelist isassociated with simultaneous exposure to multiple distinct media sourcesduring a second time period; and responsive to determining that thepanelist is associated with simultaneous exposure to multiple distinctmedia sources during the second time period, augmenting the backfilledmedia monitoring data for the panelist to indicate that the second timeperiod included simultaneous media exposures to multiple distinct mediasources.
 9. The method of claim 8, further including obtaining thecensus data corresponding to the panelist by filtering anonymized censusdata using an anonymized identifier corresponding to the panelist. 10.The method of claim 9, further including determining the anonymizedidentifier corresponding to the panelist.
 11. The method of claim 9,wherein the anonymized census data corresponds to media accessed by aplurality of media devices, the anonymized census data includinganonymized identifiers for the plurality of media devices.
 12. Themethod of claim 8, further including crediting media exposure for thepanelist based on the augmented backfilled media monitoring data. 13.The method of claim 8, further including obtaining the media monitoringdata for the panelist from a meter device associated with the panelist.14. The method of claim 13, wherein the meter device is configured togenerate the media monitoring data based on ambient audio samples.
 15. Anon-transitory computer readable storage medium having stored thereininstructions which, upon execution by at least one processor, causeperformance of a set of operations comprising: determining that censusdata corresponding to a panelist includes first media data that is notincluded in media monitoring data for the panelist during a first timeperiod; responsive to determining that the census data corresponding tothe panelist includes first media data that is not included in the mediamonitoring data for the panelist, generating backfilled media monitoringdata for the panelist by supplementing the media monitoring data for thepanelist during the first time period with the first media data;determining, based on media data in at least one of the census data andthe media monitoring data, that the panelist is associated withsimultaneous exposure to multiple distinct media sources during a secondtime period; and responsive to determining that the panelist isassociated with simultaneous exposure to multiple distinct media sourcesduring the second time period, augmenting the backfilled mediamonitoring data for the panelist to indicate that the second time periodincluded simultaneous media exposures to multiple distinct mediasources.
 16. The non-transitory computer readable storage medium ofclaim 15, wherein the operations further include obtaining the censusdata corresponding to the panelist by filtering anonymized census datausing an anonymized identifier corresponding to the panelist.
 17. Thenon-transitory computer readable storage medium of claim 16, wherein theoperations further include determining the anonymized identifiercorresponding to the panelist.
 18. The non-transitory computer readablestorage medium of claim 16, wherein the anonymized census datacorresponds to media accessed by a plurality of media devices, theanonymized census data including anonymized identifiers for theplurality of media devices.
 19. The non-transitory computer readablestorage medium of claim 15, wherein the operations further includecrediting media exposure for the panelist based on the augmentedbackfilled media monitoring data.
 20. The non-transitory computerreadable storage medium of claim 15, wherein the operations furtherinclude obtaining the media monitoring data for the panelist from ameter device associated with the panelist.